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Efficient Handwritten Digit Classification using User-defined Classification Algorithm

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@article{IJASEIT5397,
   author = {R. Vijaya Kumar Reddy and U. Ravi Babu},
   title = {Efficient Handwritten Digit Classification using User-defined Classification Algorithm},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {8},
   number = {3},
   year = {2018},
   pages = {970--979},
   keywords = {digit recognition; classifier; k–nearest neighbor; support vector machines classifier; hand-written digit.},
   abstract = {In automatic numeral digit recognition system, feature selection is most important factor for achieving high recognition performance. To achieve this, the present paper proposed system for isolated handwritten numeral recognition using number of contours, skeleton features, Number of watersheds, and ratio between the numbers of foreground pixels in upper half part and lower half-part of the numerical digit image. Based on these features the present paper designed user-defined classification algorithm for handwritten digit recognition. To find the effectiveness of the proposed features, these features are given as an input for standard classification algorithms like k–nearest neighbor classifier, Support Vector Machines and other classification algorithms and evaluate the results.  The experimental result proves that the proposed features are well suited for handwritten digit recognition for both user and standard classification algorithms. The novelty of the proposed method is size invariant.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=5397},
   doi = {10.18517/ijaseit.8.3.5397}
}

EndNote

%A Reddy, R. Vijaya Kumar
%A Babu, U. Ravi
%D 2018
%T Efficient Handwritten Digit Classification using User-defined Classification Algorithm
%B 2018
%9 digit recognition; classifier; k–nearest neighbor; support vector machines classifier; hand-written digit.
%! Efficient Handwritten Digit Classification using User-defined Classification Algorithm
%K digit recognition; classifier; k–nearest neighbor; support vector machines classifier; hand-written digit.
%X In automatic numeral digit recognition system, feature selection is most important factor for achieving high recognition performance. To achieve this, the present paper proposed system for isolated handwritten numeral recognition using number of contours, skeleton features, Number of watersheds, and ratio between the numbers of foreground pixels in upper half part and lower half-part of the numerical digit image. Based on these features the present paper designed user-defined classification algorithm for handwritten digit recognition. To find the effectiveness of the proposed features, these features are given as an input for standard classification algorithms like k–nearest neighbor classifier, Support Vector Machines and other classification algorithms and evaluate the results.  The experimental result proves that the proposed features are well suited for handwritten digit recognition for both user and standard classification algorithms. The novelty of the proposed method is size invariant.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=5397
%R doi:10.18517/ijaseit.8.3.5397
%J International Journal on Advanced Science, Engineering and Information Technology
%V 8
%N 3
%@ 2088-5334

IEEE

R. Vijaya Kumar Reddy and U. Ravi Babu,"Efficient Handwritten Digit Classification using User-defined Classification Algorithm," International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 3, pp. 970-979, 2018. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.8.3.5397.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Reddy, R. Vijaya Kumar
AU  - Babu, U. Ravi
PY  - 2018
TI  - Efficient Handwritten Digit Classification using User-defined Classification Algorithm
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 8 (2018) No. 3
Y2  - 2018
SP  - 970
EP  - 979
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - digit recognition; classifier; k–nearest neighbor; support vector machines classifier; hand-written digit.
N2  - In automatic numeral digit recognition system, feature selection is most important factor for achieving high recognition performance. To achieve this, the present paper proposed system for isolated handwritten numeral recognition using number of contours, skeleton features, Number of watersheds, and ratio between the numbers of foreground pixels in upper half part and lower half-part of the numerical digit image. Based on these features the present paper designed user-defined classification algorithm for handwritten digit recognition. To find the effectiveness of the proposed features, these features are given as an input for standard classification algorithms like k–nearest neighbor classifier, Support Vector Machines and other classification algorithms and evaluate the results.  The experimental result proves that the proposed features are well suited for handwritten digit recognition for both user and standard classification algorithms. The novelty of the proposed method is size invariant.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=5397
DO  - 10.18517/ijaseit.8.3.5397

RefWorks

RT Journal Article
ID 5397
A1 Reddy, R. Vijaya Kumar
A1 Babu, U. Ravi
T1 Efficient Handwritten Digit Classification using User-defined Classification Algorithm
JF International Journal on Advanced Science, Engineering and Information Technology
VO 8
IS 3
YR 2018
SP 970
OP 979
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 digit recognition; classifier; k–nearest neighbor; support vector machines classifier; hand-written digit.
AB In automatic numeral digit recognition system, feature selection is most important factor for achieving high recognition performance. To achieve this, the present paper proposed system for isolated handwritten numeral recognition using number of contours, skeleton features, Number of watersheds, and ratio between the numbers of foreground pixels in upper half part and lower half-part of the numerical digit image. Based on these features the present paper designed user-defined classification algorithm for handwritten digit recognition. To find the effectiveness of the proposed features, these features are given as an input for standard classification algorithms like k–nearest neighbor classifier, Support Vector Machines and other classification algorithms and evaluate the results.  The experimental result proves that the proposed features are well suited for handwritten digit recognition for both user and standard classification algorithms. The novelty of the proposed method is size invariant.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=5397
DO  - 10.18517/ijaseit.8.3.5397